An actin remodeling role for Arabidopsis processing bodies revealed by their proximity interactome

Abstract Cellular condensates can comprise membrane‐less ribonucleoprotein assemblies with liquid‐like properties. These cellular condensates influence various biological outcomes, but their liquidity hampers their isolation and characterization. Here, we investigated the composition of the condensates known as processing bodies (PBs) in the model plant Arabidopsis thaliana through a proximity‐biotinylation proteomics approach. Using in situ protein–protein interaction approaches, genetics and high‐resolution dynamic imaging, we show that processing bodies comprise networks that interface with membranes. Surprisingly, the conserved component of PBs, DECAPPING PROTEIN 1 (DCP1), can localize to unique plasma membrane subdomains including cell edges and vertices. We characterized these plasma membrane interfaces and discovered a developmental module that can control cell shape. This module is regulated by DCP1, independently from its role in decapping, and the actin‐nucleating SCAR–WAVE complex, whereby the DCP1–SCAR–WAVE interaction confines and enhances actin nucleation. This study reveals an unexpected function for a conserved condensate at unique membrane interfaces.


Introduction
Proteins may participate in multivalent interactions with themselves or one another. Multivalency can depend on weak interactions between charged residues, dipoles, and/or aromatic groups often displayed by so-called "intrinsically disordered regions" (IDRs). These weak interactions can promote their dissociation from the bulk protein pool to form droplet-like assemblies through, for example, liquid-liquid phase separation (LLPS; Beutel et al, 2019). LLPS is a state favoring condensation through weak intra-or intermolecular interactions. We use the term "condensates" hereafter to describe such proteinaceous assemblies. LLPS occurs when the bulk concentration exceeds a threshold above which molecules spontaneously partition into condensates that can resemble droplets due to surface tension. Transient or more stable condensates form in the nucleus, cytoplasm, or plasma membrane (PM) interfaces, for example, the animal-specific junction adherent molecules (Beutel et al, 2019;Zaccara & Jaffrey, 2020).
Condensates can coarsen, increase or decrease in size over time on membrane surfaces (Snead et al, 2022). Likewise, the archetypal and evolutionarily conserved condensates known as processing bodies (PBs), which are rich in proteins and RNA and modulate RNA silencing, decapping and decay, undergo fission at membrane surfaces of the endoplasmic reticulum (ER) through an unknown mechanism (Lee et al, 2020). In accordance with their functions, PBs contain decapping and exosome complexes, deadenylases, RNAs, and RNA-binding proteins. In PBs, including in plants, proteins such as the RNA decapping complex comprising mainly DECAPPING 1 (DCP1) and DCP2, and the proteins DCP5, VARI-COSE (VCS), Protein Associated with Topoisomerase II 1 (PAT1), as well as the EXORIBONUCLEASE 4 (XRN4) localize there (Xu et al, 2006;Xu & Chua, 2009;Rymarquis et al, 2011;Roux et al, 2015). Although systematic studies of plant PBs are lacking, animal PBs appear to be long-term storage sites for mRNAs poised to be released for translation following specific cues related to stress, metabolism, and translation capacity.
Albeit readily visible in cells, as aforementioned, condensates such as PBs depend on a meshwork of weak interactions and lack a surrounding membrane, which makes their isolation challenging. Proximity-dependent biotin ligation (or PDL) harnesses covalent biotinylation of interacting proteins with or near neighbors of a 1 of 26 particular prey protein; we and others have recently established PDL approaches in various plants (reviewed in Zhang et al, 2022). By bypassing the need to retain native interactions for their identification, PDL holds promise to delineate the organizational and functional principles of condensates.
Inspired by PDL applications for the elucidation of condensates in non-plant models (Youn et al, 2018), we established a PDL pipeline to determine the protein composition of condensates in plants, using PBs as a proof of concept. We show here that PBs are organized into both known and previously unknown functional modules. We further discovered that PBs components interface with PM domains, accumulating at cell edges or vertices. The suppressor of the cAMP receptor (SCAR)-WASP family verprolin homologous (WAVE) complex modulates this interface by recruiting DCP1. The SCAR-WAVE-DCP1 link enhances actin nucleation likely through the actin-related protein complex 2/3 (ARP2-ARP3; Kim et al, 2021;Lee & Szymanski, 2021;Qin et al, 2021). This SCAR-WAVE-DCP1 nexus functions as a coordinated cellular system that culminates in growth regulation.

A bait for PB proteome capture
To establish an approach for determining the protein composition of condensates in plants, we focused on PBs as a proof of concept, as non-plant PBs are omnipresent. A stepwise PDL-based approach might be the most appropriate for determining the composition of condensates, as we assumed that incorporating a standard affinity purification (AP) step before a subsequent PDL step might help distinguish between strong or direct versus the more specific interactions for condensates which are transient and weak. We thus used DCP1 as a bait tagged with both the sequence encoding for FLAG peptide and the biotin ligase "TurboID" as a construct encoding a chimeric DCP1-TurboID-6xHis-3xFLAG (driven by the 35S promoter; 35Spro:DCP1-TurboID-HF). The FLAG can be used for AP of DCP1 through a-FLAG beads and the TurboID for PDL. We describe the pipeline in Fig 1A and the technical details in the Materials and methods. As a specificity control for our assays, we used lines expressing GFP fused to 3xFLAG and TurboID (35Spro:GFP-TurboID-HF); the TurboID was described in . DCP1-TurboID-HF was functional, showing higher expression levels (~25%, P = 0.047, ANOVA) than were seen in wild-type (WT) seedlings, it could rescue the decreased size of adult plants of the dcp1-3 mutant (Martinez de Alba et al, 2015), and could lead to efficient proteome biotinylation (Fig EV1A-C). Therefore, this DCP1 fusion is a physiologically relevant bait with which to explore the PB interactome.

APEAL captured the PB proteome
We asked whether the AP/PDL combination using the DCP1 bait could indeed capture both direct interactions but also weak ones. We define the PDL results hereafter as the PBs "proxitome." After the AP step, we detected biotinylated proteins in the remaining supernatant (Figs 1A and EV2,corresponding to the "flow-through 1"). We thus decided to further consider proteins obtained from both the AP and PDL steps; this tandem analytical approach is referred to hereafter as "APEAL" (for tandemly coupled Affinity Purification with Proximity-dEpendent LigAtion steps). We coupled APEAL with nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS) in biological duplicates on DCP1 baits using 4-week-old rosettes grown under heat stress (HS) conditions (37°C for 2 h) and non-stress (NS) conditions. We selected HS treatment to benchmark APEAL because HS induces a size and number increase in PBs and likely a change in their composition (Gutierrez-Beltran et al, 2015), and it enhances RNA decapping through the increased association of DCP1-DCP2 with polysomes (Merret et al, 2013). We observed that HS does not significantly affect biotinylation levels of DCP1 (Fig EV1B, i.e., "24 h" vs. "HS", the two samples obtained under the same setting: 24 h application of biotin with or without HS), suggesting that any differences in the PB proteome between NS and HS conditions in the APEAL may be of biological relevance.
Proteome analyses yielded almost equivalent numbers of protein hits for AP and PDL steps ( Fig EV3). Unexpectedly, and despite the size increase in PBs noted above, HS led to fewer proteins being captured in PDL/HS from DCP1, suggesting that upon HS the DCP1 molecules may be spatially further confined interacting will fewer proteins (see below for a possible explanation). To test for enrichments across the different samples, we applied the same criteria described in our previous work to define the high-confidence proximity interactions . Briefly, we considered only interactions present in both biological replicates (log 2 [fold change] ≥1, n = 2, false-discovery rate [FDR] = 0.05; Source Files 1 and 2, for unfiltered and filtered data, respectively). To compare the AP and PDL datasets, we also assigned LFQ (label-free quantitation) and iBAQ values (intensity-based absolute quantification, Source Files 3 and 4; Cox et al, 2011) to the semi-quantitative peptide enrichments and reiterated interactions. The LFQ/iBAQ assignments produced similar results to the semiquantitative analysis.
An inspection of protein hits in the PDL step confirmed an enrichment for conserved PB core components ( Fig 1B; log 2 FC ˃ 1; Gutierrez-Beltran et al, 2021). As a cautionary note, we do not define the PB core as a structural entity with topological essence (e.g., with the PB center formed as an immiscible liquid), but rather as an indispensable set of critical or accessory proteins that comprises part of the decapping machinery. Intriguingly, the AP step failed to enrich for PB core components, suggesting that many weak interactions are not retained, thus validating our dual approach (Fig 1B; "AP" vs. "PDL"). Importantly, APEAL showed no enrichment for proteins exclusively associated with the other condensate known as stress granules (SGs; e.g., RNA-BINDING PROTEIN 47B [RBP47]), despite the putative physical links between the two condensates during HS (Gutierrez-Beltran et al, 2021). One exception was the class of RNA helicases which are present in both SGs and PBs (Fig 1B; log 2 FC ≥ 1.5, RH6, RH8, RH12, and the newly discovered component of PBs RH52 herein). The absence of other SG components confirmed that APEAL, at least, in this case, can provide the required spatial resolution to specifically resolve the composition of PBs and highlights the functional differences between the PBs and SGs.
Retrieval of Interacting Genes/Proteins) database (Jensen et al, 2009), revealing that the PDL dataset forms a denser PPI network than that obtained with AP. The average numbers of interactions from PDL per protein were 5.4 (P = 5.6e À11 ) and 4.1 (P = 2.1e À10 ) for NS and HS, respectively ( Fig 1C). The PDL dataset also showed a different profile in terms of interactions from AP, while different interactions were observed when HS and NS were compared (Figs 1D and 2A). Consistent with the expected incremental formation of PBs during HS, the term "PΒs" was more enriched in the PDL dataset upon HS when compared to NS (P = 6.9e À2 vs. 6.9e À22 ). We validated the results of the PDL step using bi-fluorescence complementation (BiFCs; concept described in Fig EV4A, negative control XRN3, P = 0.19 to < 5.7e À4 ) and colocalization analyses in the heterologous Nicotiana benthamiana transient expression system, showing the successful identification of novel components of PBs or DCP1 interacting proteins (Fig EV4A and B; nine out of nine proteins tested). We detected a partial overlap between our datasets and other published proteomics datasets that analyzed PM composition by AP with various baits (Source Files 5 and 6; overlap 27.1%; Fernandez-Calvino et al, 2011;Bernfur et al, 2013;Rutter & Innes, 2017), and with endomembranes (overlap 9-32.8%; Heard et al, 2015), suggesting that PBs may interface A Overview of the APEAL pipeline. Upon 24 h biotin feeding and treatment (supplied with 50 lM biotin directly into leaves of 4-week-old plants by syringe infiltration), total proteins are extracted from infiltrated leaves. The proteome is subjected to AP-immunocapture of the FLAG tag. In the AP step, some of the captured proteins will be biotinylated. The PDL step uses the leftover supernatant from the AP step and captures biotinylated proteins with streptavidin beads. PPI, protein-protein interactions; AP, affinity purification; PDL, proximity-dependent biotin ligation; FLAG IP, FLAG-beads immunoprecipitation; DYNA-IP, streptavidin-bead immunoprecipitation. We use the term "proxitome," to describe the proteins captured by the PDL step of APEAL. These proteins may not physically interact with DCP1. B Heatmap showing the "core processing body (PB)" (magenta) components identified and other linked proteins. RBP47 is a stress granule marker (SGs; blue). RH52 is a new helicase identified as a PB component (green arrow). The scale on the right shows log 2 FC of protein abundance. Note that only the PB core components were enriched in the PDL (i.e., the proxitome, log 2 FC~1 or above) but not in AP. Furthermore, heat stress (HS) increased the enrichment of some PB core components. C Comparison of AP/PDL interacting networks produced from APEAL. STRING density plots of pairwise interactions between proteins obtained from the AP or PDL steps (combined interactions found in non-stress [NS]/[HS]). Note that PDL produces an overall denser interaction network (under standard parameters, the same number of proteins was selected for AP/PDL). D Venn diagram showing the proteins identified for PDL and AP in NS and HS samples (PPIs fulfilling the criterion log 2 FC > 1).
Source data are available online for this figure.
Ó 2023 The Authors The EMBO Journal 42: e111885 | 2023 with membranes. In comparison with a recent study examining DCP1 and DCP2 interactions through AP, we found an overlap of up to 36.3% (Schiaffini et al, 2021). Notably, this comparison showed a lower-than-expected overlap with our datasets, while in this previous study similar membrane-related proteins were missing. Although in this previous study, the stabilizer of weak interactions formaldehyde was used, because it has the shortest span (~2-3 A) of any known crosslinker (Nadeau & Carlson, 2007), it may not be the best option to stabilize interactions at the interface between condensates and membrane proteins. More crosslinkers with various spans will need to be tested, but we speculate for now that proxitomes may expand identifications of new proteins in PPI studies. Furthermore, our datasets suggested that PB may interface with membranes; we clarify this in detail below.
We tested hierarchical linkages between the networks we generated by integrating the data from STRING with our datasets using hypergeometric tests (FDR = 0.05). We assigned four dense and interconnected subnetworks to PBs according to overrepresentations in AP and PDL datasets from both HS and NS conditions (Figs 2 and EV5; note that in volcano plots in PDL the DCP1 was not the most abundant). Subnetwork 1 is related to RNA metabolism ( Fig 2B). In subnetwork 2, we obtained hits for phytohormone signaling, defense attenuation, translation, and metabolism ( Fig 2C). Subnetwork 3 comprises an unexpected network for PBs, with hits linked to membrane remodeling and trafficking ( Fig 2D); finally, subnetwork 4 includes another surprising network of proteins linked to the cytoskeleton and in particular actin remodeling ( Fig 2E and Source File 7). In the Appendix text, we succinctly

A
Volcano plots showing significantly enriched proteins in NS and HS conditions from the AP and PDL APEAL steps. Selected proteins are indicated in magenta and are encoded by genes that belong to the identified subnetworks described in (C) and (D). Magenta indicates enrichment in HS samples; cyan indicates depletion in HS samples. B-E Gene Ontology (GO) enrichment analyses of the APEAL results, divided into four subnetworks. Note the terms related to vesicle trafficking and actin remodeling. A more detailed description is provided in Fig EV5 and in the Appendix text. Note that signal transduction proteins, metabolism-related proteins, and vesicle trafficking proteins evade PBs during HS ( Fig EV5, reduced hit number), while the opposite pattern is observed for actin and RNA metabolism subnetworks. FDR, false discovery rate. Important links described below are indicated (i.e., to the SCAR-WAVE/ARP2-ARP3 (and the component NAP1 which is part of the SCAR-WAVE and links to autophagy), cytoskeleton, PB core, and cell-wall-related metabolism).
Source data are available online for this figure.

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The EMBO Journal 42: e111885 | 2023 Ó 2023 The Authors describe these networks, from which testable hypotheses may arise for future studies.

DCP1 interfaces with plasma membrane domains at the cellular face independently of its role in decapping
The interfacing between condensates and membranes can be functionally important by regulating protein activities, as has been shown for animal cells (Snead et al, 2022). We thus asked whether the links between PBs and subnetwork 3, which suggested membrane functions, might relate to the decapping function of PBs.
To observe membranes in detail and expedite analyses, we focused on the PM and used total internal reflection microscopy (TIRF-M), which is suitable for viewing cell surface processes due to shallow illumination penetration (decay constant~100 nm). TIRFM allowed us to observe occasional stalling and fusion of DCP1-GFP-positive droplets with one another at the lateral PM interface of meristematic root epidermal cells (Fig 3A and B). Though encouraging, we note here that these results should not be interpreted as a direct association between DCP1 and membrane lipids. As a cautionary note also, the lateral PM used for imaging due to the TIRF-M inherent limitations which do not allow observation of apicobasal membranes, may not reflect the exact situation of these membranes. Furthermore, due to the swallow illumination depth, TIRFM does not allow observation of cells below the epidermis or the root cap in roots (e.g., cortex). Mean squared displacement (MSD) analyses, which track particles in two dimensions, showed that DCP1-positive droplets become more stable with time, after an initial Brownianmotion diffusion at the PM (Fig 3B, inset; track analyses). This tracking also showed that stable DCP1-positive droplets lose signal with time ( Fig 3B, kymograph). This result suggested that DCP1positive droplets may diffuse to form 2D domains at the inner face of the PM and highlights that subnetwork 3 may be of biological relevance. High-speed super-resolution confocal microscopy (~120 nm axial resolution and 40 frames per second) and fluorescence partition analyses of the obtained micrographs between the PM and the overall signal at the cytoplasm (in PBs or the dilute phase) detected a fraction of mScarlet-DCP1 at the PM (marked here with the auxin efflux carrier PINFORMED2 [PIN2]), regardless of the promoter driving the DCP1 ( Fig 3C, the constitutive in meristems RPS5a, the strong 35Spro or the native DCP1pro; both N-or C-tagged lines). Other core PB components were associated with the PM either very transiently (in dividing cells) or not at all (Appendix Fig S1A). We further observed that DCP1 can exhibit a polar behavior localizing at apical or basal domains of the PM, which was not observed for other core PB components (Fig 3C, lower right for polarity, and Appendix Fig S1). Moreover, the addition of cycloheximide (CHX), known to dissolve PBs as they are in dynamic equilibrium with polysomes (Gutierrez-Beltran et al, 2015), decreased PB numbers and led to greater DCP1 localization at the PM (Appendix Fig S1B). By generating and using DCP1 tagged with the photoconvertible molecule EosFP to track in vivo DCP1 molecules (Wiedenmann et al, 2004; UBQ10pro:EosFP-DCP1; photoactivatable green-to-red; Appendix Fig S1C caption for method details), we confirmed CHX results suggesting that DCP1 can shuttle between PBs and the PM (Appendix Fig S1C, and edges as described below). We cannot though discount the possibility that a fraction of DCP1 is targeted to the PM independently from PBs. Taken together, these results indicate that DCP1 localization at the PM is in a dynamic equilibrium with PBs.
Notably, some cells accumulated more overall DCP1-GFP at the PM than others, while in some cases we observed a spatial restriction of the signal at cell edges not seen for other PBs proteins or PIN2 (e.g., Fig 3C and Appendix Fig S1B). We use the term "edge" in the geometric sense of an intersection between two faces of a polyhedron, rather than of a periphery or front (Kirchhelle et al, 2016). This variability of localization prompted us to examine in detail in which cells DCP1 can be found on membranes, including the edges. To expedite the analyses here, we divided the roots into three regions (see also Fig 3A). Dynamic 3D imaging of DCP1-GFP in these regions showed that the probability to find DCP1 at cell edges was higher far from the quiescent center (QC) along the proximodistal root axis (as defined in Fig 3A), where DCP1 can be further confined at the inner face of the PM at vertices (Fig 4A, e.g., in region 3). Like "edge", the term "vertex" here is used in the geometric sense to define the internal angular point of a polygon and is defined as an intersection between the three cell edges. Under HS conditions, DCP1-GFP abundance decreased at the PM at the expense of PBs but increased along the cell edges ( Fig 4B). Our results suggested a dynamic competition between PBs and vertices exclusively for the DCP1 component.
As DCP1 localization at the edge or vertex was unaffected by the presence of CHX (Appendix Fig S1B, arrowhead), this result implied that DCP1 did not form decapping complexes at PM interfaces. To further validate this result, we used ultra-fast live-cell imaging super-resolution microscopy showing that DCP1-decorated PBs undergo fission at the PM plane, probably to remodel PBs and release DCP1 (Fig 4C). This result is consistent with the diffusible loss of material from PBs associated with the PM observed by TIRF-M ( Fig 3B; at the same regions, 2 and 3) and likely implied that DCP1 dissociates from DCP2, although whether this dissociation is a prerequisite for the further loss of material requires further studies. Likewise, in animals, PBs undergo fission by the membranous ER sheets (Lee et al, 2020), but it is also unclear whether ER plays a role in splitting PBs at the PM. Accordingly, when we tested the dynamic interaction between DCP1 and DCP2, by sensitized emission Förster resonance energy transfer (SE-FRET), we did not detect DCP1-DCP2 interaction at the PM, vertex, or edge (Appendix Fig S2A). However, DCP1 did show a weak and transient association with the PBs core protein PAT1 at the PM (but not at the edge or vertex; Appendix Fig S2A). As the decapping complex comprises DCP1 and DCP2 as minimal components (Charenton et al, 2016), together with the CHX data, these results argued against the formation of a decapping complex at the PM or on edges/vertices.
To validate the lack of association between DCP1 and DCP2 at membranes, as FRET is sensitive to stoichiometry and may also fail to capture very transient interactions, we exploited the power of quantitative 3D proximity ligation assays (PLAs;Teale et al, 2021;preprint: Liu et al, 2022). PLA uses complementary oligonucleotides fused to specific antibodies to determine the frequency with which proteins of interest find themselves nearby (< 40 nm). When the proteins of interest interact or are nearby, PLA leads to a spot-like signal (Appendix Fig S2B, left). In the PLA assay, DCP1 and DCP2 interacted or were nearby only in the 6 of 26 The EMBO Journal 42: e111885 | 2023 Ó 2023 The Authors cytoplasm but not at the PM (Fig 5). We also tested DCP1 against PAT1 (positive control) and the auxin efflux carrier PINFORMED7 (PIN7; negative control) to assess the reliability of PLA in Arabidopsis roots, with the prediction that fewer interactions should occur when target protein pairs have increasingly discrete accumulation domains (i.e., PIN7; absent in APEAL, Appendix Fig S2B and C). Unexpectedly, through this approach, we established that DCP1 and DCP2 form complexes also in the nucleus (Fig 5, inset 2), as was shown in yeast (Saccharomyces cerevisiae), where they act as a decapping reservoir (Tishinov & Spang, 2021). Together, PLA and SE-FRET analyses speak against the existence of a localized decapping activity at the PM. Furthermore, our analyses show that PLA assays can help in validating proxitomes. A Left: Gradual edge or vertex accumulation of DCP1-GFP in three different root regions. DCP1 signal intensity among the three different regions, at the edge/vertex (epidermis). Right: z cross-sectional images of DCP1-GFP (green) in the whole root compared to FM4-64 staining (magenta, staining membranes). The circular plots indicate the average DCP1 localization (regions 1-3; N, biological replicates = 3, n = 10 cells; comparing regions 1-2 and 2-3). The 3D-rendered images (PIN2-GFP vs. mSCarlet-DCP1) show the localization of mScarlet-DCP1 at edges/vertices in two different regions (in comparison to PIN2-GFP signal which decorated almost evenly the PM). Scale bars: 20 lm. Arrowhead denotes the edge (region 2) and the vertex (region 3) decorated by mScarlet-DCP1 (also in the z cross-sectional image). Note in a single cell file, how the localization from the PM changes to the edge, as indicated, along the proximodistal axis. B Representative confocal micrograph of DCP1-GFP (DCP1pro:DCP1-GFP, transgene) in root meristematic cells under NS/HS conditions (region 2). Scale bars, 5 lm. Note the depletion of DCP1 from the PM upon HS, but the increased edge/vertex signal (yellow arrowheads denote the vertex signal). Right: DCP1 signal intensity at the PM or edge/vertex (N, biological replicates = 3, n (pooled data of 3 biological replicates) = 18-23 PMs or edges/vertices). C Representative confocal images showing fusion (coarsening), fission, and growth of PBs (DCP1-positive) at the PM (region 3). Right: states of PBs (dynamic: fusion and fission and non-dynamic: stable; N, biological replicates = 2, n (pooled data of 3 biological replicates) = 6-8 PBs). As a cautionary note, the "stable" PBs may not show dynamicity in the imaging time used (~3-5 min) but later, may do.
Data information: In (A), *P < 0.05 was determined by a nested t-test. In (B), P values were determined by the Kolmogorov-Smirnoff, while in (C) by one-way ANOVA. Upper and lower lines in the violin plots when visible, represent the first and third quantiles, respectively, horizontal lines mark the median and whiskers mark the highest and lowest values. Source data are available online for this figure.

SCAR-WAVE recruits DCP1 at the cell edge/vertex
Given the reversible association of DCP1 with the PM (e.g., in HS), we postulated that DCP1 is not inserted there. We first tested whether DCP1 accumulates at the PM, edges, or vertices, through the secretory pathway by incubating seedlings with brefeldin A (BFA, 50 lM), a fungal toxin that prevents vesicle formation for exocytosis by inhibiting ADP ribosylation factor GEFs (ARF-GEFs; Satiat-Jeunemaitre et al, 1996). DCP1-GFP did not accumulate intracellularly upon BFA treatment (Appendix Fig S3), and it lacks a signal peptide or a transmembrane domain, suggesting that it is not inserted in the PM and likely is not a secreted protein.
The accumulation of DCP1 during development at cell edges and vertices prompted us to examine the underlying mechanism by which DCP1 is recruited there. We postulated that proteins with which DCP1 interacts could regulate this localization. To this end, we compared DCP1 localization to that of other proteins that localize at cell edges/vertices and are enriched in the APEAL (i.e., SOSEKI3 [SOK3], which regulates a cellular coordinate geometric system (van Dop et al, 2020) with log 2 FC = 0.58 for NS and 2.0 for HS; and the SCAR-WAVE complex (Dyachok et al, 2008) that regulates actin nucleation). In addition, as a negative control, we used the edge-localizing Ras-related protein Rab-A5c (Kirchhelle et al, 2019), which we did not identify by APEAL. SOK3 localized at the cell division zone (the cell plate fusion site), unlike DCP1, which was absent from this site (Appendix Fig S4A and B, region 1). Later in development, SOK3 showed some signal collinearity with DCP1 at edges/vertices (Pearson's correlation coefficient = 0.87, region 3).
In contrast, Rab-A5c showed little colocalization with DCP1 at all stages examined (Appendix Fig S4C). We further observed that neither the loss of SOK3 function (from a sok3 mutant), the simultaneous loss of SOK1/SOK3 functions (from genome editing in the sok3 mutant; details in Materials and methods), nor Rab-A5c depletion (through a dominant-negative dexamethasone-inducible expression of inactive Rab-A5c; Kirchhelle et al, 2016) resulted in changes in DCP1 localization at the PM or the edge/vertex (Appendix Fig S4B and C, in live imaging or through detection by a-DCP1; details for the antibody in Materials and methods). These data suggested that neither SOK3 nor Rab-A5c recruit DCP1 at vertices or edges.
In sharp contrast with Rab-A5c or SOK3, we observed almost perfect signal collinearity at vertices/edges for mCherry and GFP signals in cells co-expressing SCAR2-mCherry (with log 2 FC = 2.32 for NS and 3.62 for HS, respectively) and DCP1-GFP or BRK1-YFP (BRICK1; components of the SCAR-WAVE complex; Dyachok et al, 2008) with mScarlet-DCP1 ( Fig 6A). The SCAR2 signal though at the cell plate was weak and we were unable to draw conclusions about colocalizations there. Of note, other SCAR-WAVE components like PIR121/SRA1, NAP1, and GRL/NAP125 were also highly enriched in PDL datasets, especially upon HS (log 2 FC ≥ 2 for NS and 3.5 for HS; Fig EV5 and Source Data 7). Accordingly, upon HS, as DCP1 association with the PM decreased, we observed a stronger colocalization at edges/vertices between SCAR2 and DCP1 ( Fig 6A). This result explains well the increase in DCP1 signal at edges/vertices in HS and the APEAL results suggesting a stronger association of SCAR-WAVE/DCP1 under HS. Confocal micrographs showing single optical sections PLA-assays producing signal that resembles spots. The antibodies used were anti-FLAG/anti-GFP detecting the HF-mScarlet-DCP1/ DCP2-YFP, respectively (RPS5apro:HF-mScarlet-DCP1 and 35Spro:DCP2-YFP). Inset 1: magnification showing the colocalization of PLA spots with DCP1 or DCP2 signals (colocalization and Pearson's correlation coefficient PCC value for the spots shown). The dotted white line in the PLA channel corresponds to the PM plane. Inset 2: positive PLA signal for DCP1 and DCP2 in the nucleus. "n1-n3" correspond to nuclei regions (green circles). On the right, note the PLA spot nearby the nucleus ("detail of PLA signal"). The chart shows the quantification of PLA spots per cell at puncta (cytoplasm) or on the PM (N, biological replicates = 3, n (pooled data of 3 biological replicates) = 16-33 cells). As a cautionary technical note, the cytoplasmic, nuclear or PM "spots" do not connote physiologically relevant puncta, condensates, or PM clusters. Scale bar: 20 lm / 10 lm for the insets. Data information: In (A), *P < 0.05 was determined by one-way ANOVA. Upper and lower lines in the violin plots when visible, represent the first and third quantiles, respectively, horizontal lines mark the median and whiskers mark the highest and lowest values.

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The EMBO Journal 42: e111885 | 2023 Ó 2023 The Authors We also investigated whether the colocalization between DCP1 and SCAR-WAVE, reflected an interaction. Indeed, DCP1 and SCAR2, or DCP1 and BRK1, interacted in FRET assays in edges/ vertices but not in PBs ( Fig 6B, region 3). To ascertain these interactions, we used the quantitative 3D PLA assay ( Fig 6C). PLA determined that DCP1 and SCAR-WAVE components mainly interact at A DCP1 colocalizes with the SCAR-WAVE components SCAR2 and BRK1. Representative confocal micrographs showing colocalization between DCP1 and SCAR2 or DCP1 and BRK1 (in lines coexpressing DCP1pro:DCP1-GFP and SCAR2pro:SCAR2-mCherry or RPS5apro:HF-mScarlet-DCP1 and BRK1pro:BRK1-YFP; arrowheads indicate colocalization at cell edges or vertices) in root epidermal cells (regions 2-3). Right: relative signal intensity profiles of DCP1 or SCAR2 at vertices. Colocalization at these regions was also calculated (PCC; N, biological replicates = 2, n = 4-10 edges/vertices in regions 2-3; a slight increase was observed in HS, 0.78 in NS vs. 0.87 in HS). Scale bars: 10 lm. B Representative confocal micrographs with acceptor photobleaching-FRET efficiency between SCAR2-mCherry (up) or BRK1-mRuby (down) and DCP1-GFP (epidermal cells, regions 1 and 3). Right: normalized FRET efficiency between SCAR2 or BRK1 and DCP1, respectively, among the different developmental root regions (N, biological replicates = 2, n = 16 cells). Scale bars: 3 lm. C Representative confocal micrographs showing PLA spots produced by a-GFP/a-RFP in DCP1-GFP/SCAR2-mCherry lines and DCP1-GFP/BRK1-mRuby lines, a-FLAG/a-GFP in HF-mScarlet-DCP1/SPI-YPet (SPIRRIG [SPI] is a negative control as it localizes to PBs only during salt stress and was not found in the APEAL). In the SPI PLA, a high contrast inset is presented. Right: number of PLA spots per cell (N, biological replicates = 3, n (pooled data of 3 biological replicates) = 14-33 cells). In the merged images, the cell contours are shown (light green transparent). Scale bars: 5 lm. Ó 2023 The Authors The EMBO Journal 42: e111885 | 2023 edges/vertices, although we occasionally observed PLA spots in the cytoplasm, where SCAR-WAVE can also be found (Wang et al, 2016). We note here that AP step of the APEAL failed to retain the interaction between DCP1 and SCAR-WAVE, indicating that coimmunoprecipitation experiments are ineffective in this case (Source File 5). These results are specific, as DCP1 failed to produce positive PLA spots when combined with SOK3 at the PM or with SPIRRIG (SPI), a protein that associates with SCAR-WAVE in root hairs or with PBs only during salt stress (Steffens et al, 2015; Fig 6C and Appendix Fig S4A). Furthermore, in the quadruple loss-of-function mutants scar1234 and brk1 that show a lack of SCAR-WAVE activity (Djakovic et al, 2006;Dyachok et al, 2008;Chin et al, 2021), DCP1 localization at the edge/vertex was almost completely lost in both a live imaging setting or through detection by a-DCP1 (Fig 6D). In scar1234 and brk1, the DCP1 localization domain size at edges/vertices was expanded, in the few cells that DCP1 localized there (Fig 6D), suggesting that SCAR-WAVE is required for the confinement of DCP1 at edges/vertices. Furthermore, this reduced DCP1 localization at edges/vertices in scar1234 and brk1 was associated with an increase in the number of PBs (Fig 6D), further confirming the competition for DCP1 between PBs and membranes.
SCAR-WAVE activates the ARP2-ARP3 complex to nucleate actin through polymerization and the organization of filaments into y-branched networks (Huang et al, 2019). ARP2-ARP3 complex components were slightly enriched in the APEAL datasets, again mainly under HS like the SCAR-WAVE components likely due to the local restriction of ARP2-ARP3 there (log 2 FC~1.8 for HS; Fig EV5). The major ARP2-ARP3 component ARPC5 expressed as a tagRFP fusion (the smallest subunit of the ARP2-ARP3 complex), 10 of 26 The EMBO Journal 42: e111885 | 2023 Ó 2023 The Authors colocalized partially with DCP1 and showed a significant interaction with it in FRET only at vertices (Appendix Fig S5A). Notably, the loss of ARP2-ARP3 function (in the "crooked" mutant allele), arpc5 (Pratap Sahi et al, 2017) or the ARP2-ARP3 inhibitor CK-666, did not deplete DCP1 from the vertex/edge, but rather led to a more variable DCP1 localization domain size at edges/vertices (Appendix Fig S5B). These results suggested that SCAR-WAVE may be involved in the recruitment of DCP1 molecules at the vertex, while the SCAR-WAVE effector ARP2-ARP3 may contribute to the confinement of the SCAR-WAVE-DCP1 at vertices.

DCP1 Phosphostatus defines its localization at the edge or vertex where it regulates Actin remodeling
As DCP1 interacted with SCAR-WAVE and ARP2-ARP3, we aimed at testing whether DCP1 might affect actin remodeling. DCP1 showed almost perfect signal collinearity with cortical F-actin (decorated by the UBQ10pro:LifeAct-mCherry; Fig 7A, top). The actindepolymerizing drugs cytochalasin D or latrunculin B strongly enhanced DCP1 localization at edges or vertices (Appendix Fig S6, P < e À4 ), suggesting that actin does not recruit DCP1 there. In A Representative confocal 3D rendering micrographs of root meristematic cells from the WT or the dcp1-3 mutant stained with phalloidin for actin visualization (N, biological replicates = 3, n = 4). Scale bars: 5 lm (z-scale is 4 lm). Upper middle: the "Spectrum" micrographs indicate the maximum color-coded signal intensity (scale on the right, middle inset). Note that the signal is evenly distributed in region 1 (left upper micrograph), whereas it mostly accumulates at the edge or vertex in regions 2 and 3 (see arrowheads; images on top). Lower middle: a detail of the higher actin accumulation at edges/vertices in region 2 (compare regions 1 and 2, Scale bars: 7 lm). Insets (details, Scale bars: 2 lm) indicate the loss of vertex actin accumulation in dcp1-3. Right: plot profile from the actin signal in the WT or dcp1-3. The vertices are indicated. B Representative confocal micrographs showing actin localization in WT, dcp1-1, dcp1-3, scar1234, dcp2-1, dcp5, and pat1 upon phalloidin staining and graph (right) indicating the percentage of cells in region 3 with an accumulation of actin at edges in various genotypes (N, biological replicates = 3, n = 7-9 roots, bars show means + s.d.). Scale bars: 7 lm.  Fig S7A).
Furthermore, MTs were depleted from DCP1-rich vertices, while the actin signal was enhanced (Appendix Fig S7B and C). Intriguingly, in the apex of trichome branches, a similar MT-depletion zone A SE-FRET efficiency between DCP1-GFP or its phosphovariants with SCAR2-mCherry (among the three different root regions; mainly epidermal cells). Scale bar: 50 lm. The arrowhead denotes high FRET efficiency at edges/vertices of region 3. Right: signal quantification of SE-FRET efficiency between the indicated combinations at the epidermis of 3 regions (N, biological replicates = 6, n (pooled data of 3 biological replicates) = 10). B Actin nucleation site at an edge/vertex, as indicated by DCP1-GFP and LifeAct-mCherry localization. Right: correlation between DCP1 intensity, ACTIN, SCAR2, BRK1 and ARPC5 intensities (simple regression model). The R 2 values are shown, along with representative micrographs for DCP1/SCAR2 (N, biological replicates = 3, n = 6 for each point). C Representative confocal micrographs showing SCAR2-mCherry localization in WT and dcp1-3 mutant, respectively (root region 2, epidermal cells) and quantification of edge/vertex with SCAR2 a confined signal (N, biological replicates = 3, n = 5-8 cells). D Representative confocal micrographs showing the colocalization between DCP1-GFP or phosphovariants and SCAR2-mCherry in root meristematic cells (root region 2, epidermal cells). Scale bars: 10 lm. The insets show details of colocalization; the white arrowhead denotes the expansion of the SCAR2/DCP1 domain, while the yellow arrowheads the restricted edge/vertex SCAR2/DCP1 domains. Scale bars: 3 lm. The graph indicates the relative signal intensity for the indicated combinations (as Pearson's correlation coefficient; N, biological replicates = 3, n = 5 at edges/vertices: spread edges were not considered in calculations).
Data information: In (A and B), *P < 0.005 were determined by nested one-way ANOVA relative to the WT in the respective region. In (B), a simple linear regression (bestfitted model) with a 95% confidence interval is shown with dashed lines. In (C and D), P values were determined by an unpaired t-test. Upper and lower lines in the violin plots when visible, represent the first and third quantiles, respectively, horizontal lines mark the median and whiskers mark the highest and lowest values. Source data are available online for this figure.
12 of 26 The EMBO Journal 42: e111885 | 2023 Ó 2023 The Authors confines ARP2-ARP3 and actin (Yanagisawa et al, 2018), implying that narrow membrane domains tend to accumulate these complexes. Whether DCP1 accumulates in the trichome apex remains to be shown. Although MTs affected DCP1 confinement at edges/vertices where actin accumulated, we opted against using MTs as a tool to affect DCP1 localization, as this could lead to pleiotropic effects. We thus looked for another finer approach to affect DCP1 localization at the cell edge. As the phosphorylation of residue Ser237 of DCP1 modulates its function in PBs (Yu et al, 2019), we asked whether DCP1 phosphorylation status might also modulate DCP1 abundance at the edges/vertices. We thus introduced a construct expressing the non-phosphorylatable variant DCP1 S237A -GFP in dcp1-1 (Yu et al, 2019), a stronger allele than dcp1-3, under the control of the DCP1 promoter. We noticed an earlier and increased accumulation of fluorescence at edges/vertices in the resulting transgenic plants, compared to DCP1pro:DCP1-GFP in the dcp1-1 (Fig 7A and  B). Conversely, the introduction of the phosphomimetic variant DCP1 S237D -GFP in dcp1-1 showed a prevalent localization to PBs, alongside a pronounced inability to localize in a timely fashion to cell edges/vertices (Fig 7A and B). In the lines expressing DCP1 S237D , F-actin largely failed to accumulate at the cell edges/vertices but not on PM (Fig 7A), whereas DCP1 S237A exerted the opposite effect, enhancing actin restriction at the edge/vertex (Fig 7A). We confirmed that the scar1234, dcp1-1 and dcp1-3 mutants all display a similar lack of actin accumulation at edges/vertices, further demonstrating that actin restriction at edges/vertices increases along the developmental root axis (Fig 8Α, region 1 vs. 3). On the other hand, the core decapping mutants (e.g., dcp2-1, dcp5-1, and pat1) did not display this phenotype (Fig 8Β, right panel). Altogether, these data establish that the SCAR-WAVE-DCP1 pathway controls actin at edges/vertices in a process that can be modulated by the phosphorylation status of DCP1 Ser237 and independently of decapping.

Mutual potentiation of DCP1 and SCAR-WAVE localization at the edge/vertex
As DCP1 levels and phosphostatus at the vertex correlate well with actin nucleation, we asked if DCP1 reciprocally can affect SCAR-WAVE and ARP2-ARP3 levels there. Accordingly, we conducted FRET assays using either acceptor photobleaching or sensitized emission, along the proximodistal root axis between SCAR2 and DCP1 phosphovariants (Figs 6B and 9A). As expected, due to the increased colocalization between DCP1 S237A and SCAR2, DCP1 S237A -GFP exhibited increased FRET with SCAR2-mCherry, and a faster response to the developmental increment, unlike DCP1 S237D (Fig 9A  and C). Importantly, using signal regression analyses we managed to fit a simple regression model and observed a good correlation Remarkably, while DCP1 S237D -GFP could still localize at the edge/vertex (albeit later than the WT; 30 lm along the root axis, see also 6B quantifications), showed an expansion of the SCAR2 and ARPC5 domains (Fig 9C, insets, and Appendix Fig S8). As expected, in dcp1-3 due to the reduced levels of DCP1, the SCAR2 signal decreased at the vertex (Fig 9C). Furthermore, in DCP1 S237A the ARPC5 increased at the edges, in contrast to DCP1 S237D which showed less recruitment of ARPC5 there (Appendix Fig S8). Hence, while SCAR-WAVE recruits DCP1 at edges/vertices, DCP1 reinforces SCAR-WAVE/ARP2-ARP3 localization which may promote actin remodeling and nucleation.
The SCAR-WAVE-DCP1 nexus at the vertex can modulate growth anisotropy The SCAR-WAVE/ARP2-ARP3 module specifies leaf pavement cell shape and trichome development, light-dependent and auxindependent root growth, stomatal opening, gravitropism, salt stress responses and immunity (Chin et al, 2021 and references therein). We thus explored the possible consequences of the SCAR-WAVE-DCP1 interaction, considering also that edges are likely associated with growth anisotropy (Kirchhelle et al, 2019). Anisotropy, in terms of differential growth, is the relative change in principal dimensions over time, for example, the young hypocotyl elongates more than it widens (Kirchhelle et al, 2016). SCAR-WAVE regulates growth patterns by impinging on cell wall properties at sharp cell edges or apexes (e.g., roots or trichome; Dyachok et al, 2008; Wang et al, 2019). We thus asked whether DCP1 or SCAR-WAVE mutants showed defects in their anisotropic expansion. To attenuate the known growth perturbations of SCAR-WAVE mutants and focus on anisotropy and not on pleiotropic growth defects, we used vertically grown plates with a high concentration of gelation agent (1.5% [w/ v] vs. 0.5% gelrite), as described previously (Dyachok et al, 2011). The roots of seedlings expressing DCP1 S237D , or the progeny from dcp1-1/DCP1 plants (as the homozygote cannot survive past the early seedling stage), and of the dcp1-3 mutant, had slightly shorter roots than the WT (Fig 10A). However, seedlings expressing DCP1 S237A had longer roots than WT seedlings (Fig 10A). Albeit with mild developmental defects, dcp1-1 exhibited a significant loss of anisotropy like that seen in the scar1234, brk1, or arpc5 mutants (Fig 10B). This phenotype correlated well with actin accumulation at edges, as dcp2-1 (including the homozygous mutant, inset in Fig 10A), dcp5-1, pat1-1, or xrn4-5 or mutants lacked similar defects in anisotropy, although they did exhibit reduced overall growth (Fig 10A and B). These results suggested that SCAR-WAVE-DCP1 regulates anisotropy likely through actin.
To consolidate the link between DCP1 and anisotropy, we used isoxaben, a cellulose biosynthesis inhibitor that promotes isotropic growth (Tateno et al, 2015). Long treatments with isoxaben increased the accumulation of DCP1-positive PBs (Appendix Fig S9A), perhaps by activation of a stress-related pathway linked to cell wall integrity, so we determined a concentration and incubation time resulting in minimal effects on DCP1 localization for the following experiments (either 10 lM for 8 h or 40 lM for 1 h). Under this setting, isoxaben induced a marked isotropic cell expansion in DCP1 S237D cells mutants compared to WT, in contrast to DCP1 S237A which were more tolerant to this treatment (Appendix Fig S9B, note the isotropy graph). Furthermore, isoxaben redistributed SCAR-WAVE and DCP1 in a similar and expanded domain before isotropy and radial growth took place (Appendix Fig S9B). Notably, the DCP1 S237A cells elongated faster than those of the WT (Appendix Fig S9B, length graph). If DCP1 did not have a function in isotropy, one would expect DCP1 S237A cells to simply swell more due to higher expansion propensity, which was not the case (Appendix Fig S9B). Because of this restriction of the radial growth by DCP1 S237A , this result along with the observed defects of anisotropy in scar1234, arpc5, and dcp1 mutants support the notion that the SCAR-WAVE-DCP1 link regulates, drives, and targets expansion anisotropy.

Discussion
We propose a framework describing how the composition of a cellular condensate (PBs here) can be determined and use up this information to delineate new pathways, such as those relevant to growth. We further exemplify how a condensate is dissolved at an unappreciated membrane interface and how a fraction of the condensate can be repurposed as a cellular coordinate geometric system through the formation of another condensate (i.e., the SCAR-WAVE-DCP1 link). Unlike other coordinate systems like that driven by SOSEKI that appears important early in development (van Dop et al, 2020), the system suggested herein might be important later during development to regulate anisotropy and directional expansion.
The plasticity of PBs due to the inherent properties of condensates that depend on weak interactions allows a dynamic competition between PBs and membrane surfaces for the same components (e.g., DCP1). The subcellular positioning of DCP1 at certain membrane surfaces, and particularly at vertices, may be instrumental in driving phase transitions (i.e., phase separation), by further reducing the radius within which proteins can diffuse, thereby promoting condensation (Freeman Rosenzweig et al, 2017). This mechanism may further expand our comprehension of how condensation is promoted by reducing diffusion dimensionality (3D to 2D) from the cytoplasm to the plane of membranes. The dynamic spatial restriction of condensation also influences the material states of condensates in neurons (Gopal et al, 2017). Material state transitions (e.g., liquid-to-solid) of condensates depend on posttranslational modifications, raising the intriguing possibility that a combination of vertex confinement for DCP1 (reduced diffusion and spatial restriction), together with DCP1 dephosphorylation, may entropically favor transitions that stabilize SCAR-WAVE-DCP1.
Moreover, cell edges are sites of actin nucleation in plants (Ambrose et al, 2011 and results herein), making the link between DCP1 and SCAR-WAVE highly relevant. Similarly, in animal cells, LLPS promotes the clustering of receptors with WASP to potentiate the ARP2-ARP3 complex assembly at the PM for an efficient downstream signaling amplification (Case et al, 2019). In animal cells, many actin-nucleation-promoting factors, such as WHAMM (WASP homolog-associated protein with actin, membranes, and microtubules), JMY (Junction Mediating And Regulatory Protein), the WASH complex, and the SCAR-WAVE complex, can activate ARP2-ARP3 . However, of the above list, only the SCAR-WAVE complex has been identified in plants thus far. Plants

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The EMBO Journal 42: e111885 | 2023 Ó 2023 The Authors may thus employ other regulators to fulfill their needs for SCAR-WAVE condensation and actin nucleation. Vertices may therefore promote the activation of SCAR-WAVE through DCP1. There are also some thought-provoking parallels between plant edge/vertex condensation and animal epithelial sheets, where tight junctions (showing geometric similarities to vertices) are formed by zonula occludens condensates which maintain epithelial functions (Bosveld et al, 2018;Beutel et al, 2019). Notably, although the edgedecorating plant SOKEKI proteins contain a PDZ domain (also known as DHR domains or GLGF repeats) that is also associated with tight junctions (Beutel et al, 2019), we did not find at the moment links to SOSEKIs, whose exact functions remain to be determined. On the other hand, the links to actin nucleation are more solid, although further studies are needed to delineate the exact molecular mechanism by which SCAR-WAVE-DCP1 modulates actin dynamics.
Perhaps the most puzzling contradiction in our data is the variable edge/vertex decoration by SCAR-WAVE or DCP1. The lack of robustness of this process may relate to a seemingly stochastic condensation of SCAR-WAVE-DCP1 at regions closer to the QC, which can bring about local asymmetries in anisotropy at the cellular level. We accordingly show that cells with edges/vertices well defined by this complex follow a highly predictable anisotropic growth pattern, while cells with less determined SCAR-WAVE-DCP1 vertices have more diffusible growth patterns. Symmetry breakage, therefore, may entail a randomized condensation step that can bring about local asymmetries. Intriguingly, such cellular asymmetries underpin symmetries at the tissue level (Bou Daher et al, 2018). Lastly, feedback between the cell wall and SCAR-WAVE-DCP1 may add more complexity to this system. We thus cannot discount links between SCAR-WAVE-DCP1 and the cell wall, which can rigidify the rich in pectin middle lamella, the region between tricellular junctions (see also Fig 2C, pectin). The ARP2-ARP3 may also play a role in this context by transducing cell wall changes, as branched actin networks could be mechanosensitive (Papalazarou & Machesky, 2021).

Plant materials
All the plant lines used in this study were in the Arabidopsis Columbia-0 (Col-0) accession except the ones indicated below. Primers used for genotyping and cloning are described in Appendix Tables. Except for the Transfer (T)-DNA insertion mutants obtained from the NASC (The Nottingham Arabidopsis Stock Centre) as indicated in the Reagents and Tools table above, the following mutants and transgenic lines used in this study were described previously  To delete the complete coding sequence of the gene, we targeted two sites near the start codon of SOK3, and one site downstream of the stop codon, along with equivalent sgRNAs for SOK2 (AT5G10150). Following the published protocol, we cloned all sgRNAs into a single pRU292 destination vector. This vector allows for zCas9i expression under the UBi4.2 promoter, and expression of the sgRNAs and has a FastGreen selection marker. sgRNAs and oligonucleotide sequences used for cloning are shown in Source  Table 1. The resulting binary vector was transformed into the sok1 mutant background using the floral dipping method. We selected green fluorescing seeds and screened the resulting seedlings for large deletions in SOK3 and the absence of large deletions in SOK2 using PCR. We validated the mutations in SOK3 by direct genespecific sequencing and crossed out the transgene by selecting nonfluorescent seeds in the T2 generation. We confirmed that SOK2 was not deleted in this line, but there were small deletions in this gene.
In the T3 generation, we selected double homozygous sok1 sok3 plants.

Plant growth conditions
Arabidopsis seedlings were sterilized and germinated on halfstrength Murashige and Skoog (MS) agar medium under long-day conditions (16 h light/8 h dark). In all experiments involving the use of mutants or pharmacological treatments, the medium was supplemented with 1% (w/v) sucrose or as otherwise specified. Arabidopsis plants for crosses, phenotyping of the above-ground part, and seed collection were grown on soil in a plant chamber at 22°C/19°C, 14-h-light/10-h-dark or 16-h-light/8-h-dark cycles, and light intensity 150 lE/m 2 /s. Arabidopsis and Nicotiana benthamiana plants were grown in Aralab or Percival cabinets at 22°C, 16-hlight/8-h-dark cycles, and a light intensity of 150 lE/m 2 /s.

Phenotypic analysis and drug treatments
For quantification of phenotypes, seedlings were surface sterilized and grown on half-strength MS medium plates with 1% (w/v) sucrose. For a given genotype, differential contrast interference (DIC) images were captured on a Leica DM2500, Leica DM6B, or Leica DM6000. To define root length, images were captured from the plates using a Leica DM6000 with a motorized stage and computationally compiled together. Root length or size was determined using Bacterial strains, cloning, and constructs Electrocompetent Agrobacterium (Agrobacterium tumefaciens) strain C58C1 Rif R (pMP90) or GV3101 Rif R (i.e., a cured nopaline strain commonly used for infiltration) was used for electroporation and Nicotiana benthamiana infiltration. The Goldengate-compatible TurboID vector (35Spro:sGFP-TurboID-HF) was previously described . Cloning was done according to standard Goldengate cloning procedures. In brief, TurboID was synthesized with BsaI overhangs using the codon optimization tool of Integrated DNA Technologies for codon-optimized expression in Arabidopsis (Eurofins , FLXL1, EIN2, VAP27-1, and At5g53330 were generated by Gateway cloning. pENTR vectors were generated via BP reaction with pDONR221 (Invitrogen) and PCR product amplicons from RT-PCR using cDNA from 1-week-old seedlings. As destination vectors, pSITE17 (nYFP-GW), pSITE18 (cYFP-GW), and pG5 vector (custommade Gateway vector with tagRFP) were used.

On-beads digestion
After immunoprecipitation and extensive washing with 25 mM NH 4 HCO 3 , 0.1 lg trypsin in 10 ll of 2 mM CaCl 2 , 10% (v/v) acetonitrile, and 25 mM NH 4 HCO 3 was added to beads and incubated at 37°C overnight. Fresh trypsin (0.1 lg) in 10 ll 25 mM NH 4 HCO 3 was added to beads and incubated for another 4 h. The digested supernatant was transferred into a clean centrifuge tube, and acetonitrile was evaporated under a vacuum. The samples were then acidified and desalted using a C18 stage tip before being analyzed by nano-liquid chromatography-tandem mass spectrometry.
Liquid chromatography-tandem mass spectrometry Samples were analyzed by LC-MS using a Nano LC-MS/MS apparatus (Dionex Ultimate 3000 RLSCnano System) interfaced with an Eclipse Tribrid mass spectrometer (Thermo Fisher Scientific). Samples were loaded onto a fused silica trap column (Acclaim PepMap 100, 75 lm × 2 cm; Thermo Fisher Scientific). After washing for 5 min at 5 ll/min with 0.1% (v/v) trifluoroacetic acid (TFA), the trap column was brought in-line with an analytical column (Nanoease MZ peptide BEH C18, 130 A, 1.7 lm, 75 lm × 250 mm, Waters) for LC-MS/MS. Peptides were fractionated at 300 nl/min using a segmented linear gradient 4-15% B in 30 min (where A: 0.2% formic acid, and B: 0.16% formic acid, 80% acetonitrile), 15-25% B in 40 min, 25-50% B in 44 min, and 50-90% B in 11 min. Solution B was then returned to 4% for 5 min for the next run. The scan sequence began with an MS1 spectrum (Orbitrap analysis, resolution 120,000, scan range from m/z 350-1,600, automatic gain control (AGC) target 1E6, maximum injection time 100 ms). The top S (3 s) and dynamic exclusion of 60 s were used for the selection of parent ions for MS/MS. Parent masses were isolated in the quadrupole with an isolation window of 1.4 m/z, automatic gain control (AGC) target 1E5, and fragmented with higher-energy collisional dissociation with a normalized collision energy of 30%. The fragments were scanned in Orbitrap with a resolution of 30,000. The MS/MS scan range was determined by the charge state of the parent ion, but the lower limit was set at 100 amu.
Database search LC-MS/MS data were analyzed with Maxquant (version 1.6.10.43) with the Andromeda search engine. The type of LC-MS run was set to 1 (label-free). LC-MS data were searched against The Arabidopsis Information Resource (TAIR) plus a common contaminant database. Protease was set as trypsin, which allowed two miscuts. N-terminal acetylation and oxidation at methionine were set as variable modifications. Maximum two variable modification was allowed. Protein and peptide false discovery rate (FDR) were set to 1%. Reverse hit and common contaminants, as well as proteins identified only by modified sites, were removed. GO term analysis was performed using a combination of the Panther database and BioConductor package in R (Bonnot et al. 2019).

Visualization of networks and analyses
Cytoscape 3.5.1 was used. Tab-delimited files containing the input data were uploaded. Unless otherwise indicated, the default layout was an edge-weighted spring-embedded layout, with NormSpec used as edge weight. Nodes were manually re-arranged from this layout to increase visibility and highlight specific proximity interactions. The layout was exported as a PDF and eventually converted to a. TIFF file with Lempel-Ziv-Welch (common name LZW) compression.

Agrobacterium-mediated transient transformation of Nicotiana benthamiana
N. benthamiana plants were grown under normal light and dark regimes at 25°C and 70% relative humidity. Three-to four-week-old N. benthamiana plants were watered from the bottom~2 h before infiltration. Transformed Agrobacterium strain C58C1 Rif R (pMP90) or GV3101 Rif R harboring the constructs of interest were used to infiltrate N. benthamiana leaves and for transient expression of binary constructs by Agrobacterium-mediated transient infiltration of lower epidermal leaf cells. Transformed Agrobacterium colonies were grown for~20 h in a shaking incubator (200 rpm) at 28°C in 5 ml of yeast extract broth (YEB) medium (5 g/l beef extract, 1 g/l yeast extract, 5 g/l peptone, 0.5 g/l MgCl 2 , and 15 g/l bacterial agar), supplemented with appropriate antibiotics (i.e., 100 g/l spectinomycin). After incubation, the bacterial culture was transferred to 15-ml Falcon tubes and centrifuged (10 min, 5,000 g). The pellets were washed with 5 ml of infiltration buffer (10 mM MgCl 2 , 10 mM MES pH 5.7), and the final pellet was resuspended in infiltration buffer supplemented with 100 lM acetosyringone from a stock diluted in dimethyl formamide. The bacterial suspension was diluted with infiltration buffer to adjust the inoculum cell density to a final OD 600 value of 0.2-1.0. The inoculum was incubated for 2 h at room temperature before infiltration into N. benthamiana leaves by gentle pressure infiltration of the lower epidermis of leaves (fourth and older true leaves were used, and about 4/5-1/1 of their full size) with a 1-ml hypodermic syringe without a needle.
TIRFM imaging and tracking analyses TIRF microscopy images were acquired using MetaMorph software on an Olympus IX-81 microscope. A DV2 image splitter (MAG Biosystems) was used to separate GFP and RFP emission signals. Time-lapse movies were obtained at 100-ms intervals. For MSD analysis, 30-s-long movies with 100-ms intervals and 200-ms exposure were used. Particle tracking was limited to the amount of time that the plasma membrane remained at the focal plane; the median track length was 2,000 frames, corresponding to 3.5 s of imaging. The tracking of particles was performed with the Mosaic suite of Fiji or NanoTrackJ/TrackMate, using the following parameters: radius 3 of fluorescence intensity, a link range of 1, cutoff of 0.1%, and a maximum displacement of 8 px, assuming Brownian dynamics.

Quantification of fluorescent intensity, FRAP, and FRET
To create the most comparable lines to measure the fluorescence intensity of reporters in multiple mutant backgrounds, we crossed homozygous mutant plants carrying the marker with either a wildtype plant (to yield progeny heterozygous for the recessive mutant alleles and the reporter) or crossed to a mutant only plant (to yield progeny homozygous for the recessive mutant alleles and heterozygous for the reporter). Fluorescence was measured as a mean integrated density in regions of interest (ROIs) with the subtraction of the background (a proximal region that was unbleached and had less signal intensity than the signal of the ROI region). FRAP mode of Zeiss 780 ZEN software was set up for the acquisition of 3 prebleach images, 1 bleach scan, and 96 post-bleach scans (or more). Bleaching was performed using the 488-, 514-, and 561-nm laser lines at 100% transmittance and 20-40 iterations depending on the region and the axial resolution (iterations increased in deeper tissues to compensate for the increased light scattering). In FRAP the width of the bleached ROI was set to 2-10 lm. Pre-and postbleach scans were at minimum possible laser power (0.8% transmittance) for 458 nm or 514 nm (4.7%) and 5% for 561 nm; 512 × 512 8-bit pixel format; pinhole of 181 lm (> 2 Airy units) and zoom factor of 2.0. The background values were subtracted from the fluorescence recovery values, and the resulting values were normalized by the first post-bleach time point and divided by the maximum fluorescent time-point set maximum intensity as 1. The objective used was a plan-apochromat 40× with NA = 1.2 M27 (Zeiss). The following settings were used for photobleaching DCP1: 10-20 iterations for DCP1-GFP; 10 to 60 s per frame; 100% transmittance with the 458-to 514-nm laser lines of an argon laser. Pre-and post-bleach scans were at minimum possible laser power (1.4 to 20% transmittance) for the 488-nm and 0% for all other laser lines, 512 × 512pixel format, and zoom factor of 5.1. The fluorescence intensity recovery values were determined, the background values were subtracted from the fluorescence recovery values, and the resulting values were normalized against the first post-bleach time point. SE-FRET analyses were conducted using the method described previously (preprint: Liu et al, 2022) and photoacceptor FRET was conducted with the method as described (Karpova et al, 2003). Image analyses and intensity measurements (Integrated Density) were done using Fiji v. correcting noise using the Gaussian blur option and pixel width set to 1.0.

Statistics
The numerical data used in this publication are provided in Source Data as raw .csv files with the corresponding headings. Statistical analyses were performed in GraphPad (https://graphpad.com/) or R studio (R-project.org). Graphs were also generated by GraphPad Prism or R. PCC is calculated via Fiji, coloc2 tool. All statistical data show the mean AE s.d. (box plots) or the distribution of values (violin plots, kernel density) of at least three biologically independent experiments or samples, or as otherwise stated. Individual data points are on the plots. For violin plots, datasets were smoothed using heavy smoothing which gives a better idea of the overall distribution. In captions, N denotes biological replicates, and "n" technical replicates or population size. Each data set was tested whether it followed normal distribution when N ≥ 3 by using the Shapiro normality test. The significance threshold was set at P < 0.05 (significance claim), and the exact P values are shown in the graphs or as asterisks. Details of the statistical tests applied, including the choice of the statistical method, are indicated in the corresponding figure caption. In boxplots or violin plots, upper and lower box boundaries, or lines in the violin plots when visible, represent the first and third quantiles, respectively, horizontal lines mark the median and whiskers mark the highest and lowest values. For regression analyses, the confidence intervals were calculated and are also shown (95% confidence band) of the best-fit line. For Wilcoxon, P values are two-tailed, for Kruskal-Wallis P values are approximate, for Welch-ANOVA or Welch-Forsythe in multiple comparisons, P values are adjusted. For Kolmogorov-Smirnoff, P values are approximate. To increase the robustness of analyses when the normality was marginal (judged by the P), sometimes more than one test was used as indicated in the caption (data analyses). For acceptor photobleaching to determine the FRET-efficiency, FDR corrections were done through two-stage step-up method of Benjamini, Krieger, and Yekutieli. In one crucial experiment (dcp1-3/SCAR2 localization), the researcher did not know the genotype, which was determined later (blind experiment). In all other cases, the experiments were not blind.

Data availability
The data generated or analyzed during this study are included in this published article. Additional information and materials generated for and/or reported in this article are available from the corresponding author upon request. The mass spectrometry proteomics data from this publication have been deposited to the ProteomeXchange Consortium via the PRIDE https://www.ebi.ac.uk/pride/ partner repository (Perez-Riverol et al, 2022) with the dataset identifier PXD037701 (https://www.ebi.ac.uk/pride/archive/projects/ PXD037701).
Expanded View for this article is available online.

Disclosure and competing interests statement
The authors declare that they have no conflict of interest. A The DCP1-TurboID-HF construct efficiently rescues the adult dcp1-3 mutant phenotype. Phenotypes (3-week-old) of adult plants expressing 35Spro:DCP1-TurboID-HF and RPS5apro:HF-mScarlet-DCP1 in the dcp1-3 mutant background. In a semi-controlled greenhouse setting (temperature control 22°C), the dcp1-3 mutant showed a smaller adult stature. Lower: complementation quantification (rosette diameter), N, biological replicates = 4, n = (pooled data of 3 biological replicates) 5-7 rosettes, error bars are (mean + SD) and RT-qPCR analyses for the quantification of DCP1 expression levels. PP2A and Actin7 were used for normalization (1-week-old) seedlings (N, biological replicates = 2, n (pooled data of 3 biological replicates) = 3, error bars are mean + SD). When DCP1 was driven by the RPS5a promoter (stem cell-specific promoter), we observed partial complementation, suggesting that DCP1 is important also in non-meristematic cells. B Immunoblot analyses of lines expressing sGFP-TurboID-HF or DCP1-TurboID-HF upon NS or HS conditions (same as used for APEAL). The immunoblots also show the accumulation of auto-biotinylated sGFP-TurboID-HF or DCP1-TurboID-HF in a time course of biotin administration (as detected with streptavidin-HRP, which captures biotinylated proteins). 50 lM Biotin was delivered in leaves by syringe infiltration and diffusion. Note that HS did not increase the biotinylation efficiency of DCP1-TurboID-HF: at 24 h, compare samples "2" with "4" in the "DynaIP". HF, 6xhis-3xFLAG. The red arrowhead indicates the position of DCP1-TurboID-HF and green for sGFP-TurboID-HF (N, biological replicates = 2). We used the same scheme for biotin application as determined in . The 2 h NS/HS corresponds to the timing after the administration of biotin for 24 h (t = 0 corresponds to 24 h biotin administration in NS conditions). The red asterisk indicates non-specific band. a-FLAG was used for the detection of DCP1-TurboID-HF and sGFP-TurboID-HF (similar size~130 kDa). a-Tubulin and ponceau staining were used for loading control. C Representative confocal micrographs showing that the localization of DCP1 (a-FLAG, 5-day-old seedlings, root cap cells) to PBs is retained in lines expressing DCP1-TurboID-HF. As a control, the GFP-TurboID-HF line was used that does not show localization to PBs. Right: number of DCP1-positive foci in the corresponding lines expressing DCP1 (N, biological replicates = 1, n = 32-60 cells).  Immunoblot analyses from lines expressing GFP-TurboID-HF or DCP1-TurboID-HF (denoted as GFP or DCP1, respectively) showing the presence of biotinylated proteins in the flow-through after the AP-step (for flow-through 1, see DYNA-IP here). The blot was overexposed to detect the faint streptavidin smear in flowthrough 1. The baits in these experiments undergo auto-biotinylation, as reported previously. The results are representative of one experiment performed three times. Note that the immunodetectable biotinylation levels did not correlate well with the hits identified in Fig EV5 ( Figure EV3. Hits from the APEAL approach (AP and PDL steps).
Total protein hits from mass spectrometry under NS or HS conditions following the AP (left) or PDL (right) steps of APEAL. We used the same scheme for biotin application, as described . The results presented are unfiltered, containing the noisy portion of the proteome. Note that the free diffusion of GFP in vivo led to increased proteins identified. sGFP-TurboID-HF, GFP; DCP1-TurboID-HF, DCP1 (N, biological replicates = 3, error bars are mean AE SD). Note the increased numbers of hits in GFP/PDL reflect the noisy proteome. GFP is expected to produce more noise (translated as hits in the context of the proteome), due to the increased diffusion over the specifically and topologically restricted DCP1 (confirmed in Fig EV1, localizations). The decreased number of hits for HS conditions corresponds mainly to proteins of signal transduction and metabolism, as well as vesicle trafficking proteins (see also Fig 2 and below for an explanation: HS reduces the DCP1 association with the PM). Furthermore, DCP1, as described below, loses localization at the PM during HS.
Data information: AP/PDL GFP samples did not differ at P < 0.005 (determined by an unpaired t-test); ***P ≤ 0.05, as determined by an unpaired t-test for comparison between HS and NS GFP samples.

Chen Liu et al
The A Bimolecular fluorescence complementation (BiFC) assay of the indicated proteins. Left: the cartoon depicts the BiFC concept and YFP reconstitution caused by protein-protein interaction in vivo. When two proteins interact, the cYFP and nYFP halves are brought in proximity and produce a fluorescent signal. For protein selection, we classified the associations with DCP1, in both AP and PDL steps, according to their relative enrichment (log 2 FC). We selected proteins presenting moderate relative enrichment in the AP step, while having high predictability in the PDL step (log 2 FC > 0 in both steps). As an additional filter, we selected proteins rich in IDRs (half of the proteins from the list have high prion-like domain (PRD) scores and high Finum [aa]/total [aa] ratios as defined through the PLAAC algorithm; Source File 8), and as such proteins would be likely to localize to condensates. We tested the association between PBs (using DCP1 as one representative component) and selected five proteins from these bins using BiFC and colocalization assays (Source File 8). These results suggested that the APEAL approach may help identify PB components or DCP1 interactors. These interactions should be further studied in Arabidopsis. BiFC efficiency was estimated from the reconstituted YFP raw signal intensity and YFP-positive puncta per cell in maximum projections (N, biological replicates = 3, n = 4-12 cells). Lower left: representative confocal micrographs showing that YFP signal is reconstituted at cellular puncta that most likely correspond to PBs. XRN3 represents negative control (see also B), as it localizes in the nucleus. Right: number of cellular puncta per total cell volume (in maximum projection images; N, biological replicates = 3, n (pooled data of 3 biological replicates) = 20 cells, error bars are mean + SD). Scale bars: 10 lm. B Colocalization of selected proteins with DCP1-CFP-positive puncta (35Spro:DCP1-CFP transgene). The coding sequences of the corresponding "interactors" (PPIs; direct or indirect, defined in APEAL) were driven by the 35Spro and cloned in frame with mCherry at their 5 0 end. Two-color colocalizations were estimated by Pearson's correlation coefficients (PCC) using ultra-fast super-resolution microscopy combined with image deconvolution (~120 nm axial resolution). Numbers in "merge" indicate colocalization frequency between DCP1-CFP and the corresponding interacting protein (N, biological replicates = 2, n = 5 cells). Yellow arrowheads indicate colocalization and red arrowheads lack of colocalization. Lower right: PCC of pixel intensities between DCP1-CFP and the corresponding putative interacting protein (N, biological replicates = 3, n = 4-12 cells, error bars are mean + SD). We confirmed the ECT domain-containing proteins, MLN51, FLXL1, EIN2, VAP27-1, uncharacterized AT1G33050 (hypothetical protein), and AT5G53330/AT2G26020 (ubiquitin-associated/translation elongation factors EF1B) as novel PB components. By applying a preselection criterion of enrichment (log 2 FC > 0.5) for the selection of prey, we significantly increased the probability of identifying successful binary interactions between PBs (i.e., cYFP-DCP1) and the identified proteins. All preys were confirmed as PB components, while PDL had higher interaction predictive power than the AP step irrespective of whether proteins were enriched in NS or HS. XRN3 was used as a threshold control (log 2 FC = À0.58, PDL/NS conditions). The heatmap shows the enrichment of these proteins in the different conditions; the scale at right shows log 2 FC.
Data information: In (A and B), P values were determined by ordinary one-way ANOVA (differences were all calculated compared to XRN3). Source data are available online for this figure.  .......................................................................................................... 3 Appendix C EosFP-DCP1 tracking in live cell imaging. The monomeric EosFP (Eos: in Greek mythology the personification of the dawn), emits strong green fluorescence (516 nm) that changes to red (581 nm) upon near-UV irradiation at 405 nm because of a photo-induced modification involving a break in the peptide backbone next to the chromophore. The possibility to locally change the emission wavelength makes EosFP a superb marker for experiments aimed at tracking the movements of biomolecules within the living cell. The cartoon shows a PB that is decorated with EosFP-DCP1 and is photoconverted to red. If the signal turns green again, it means that DCP1 molecules are exchanged. Representative confocal micrographs showing the signal conversion of PBs or cytosolic DCP1 (UBQ10pro:EosFP-DCP1) (N, biological replicates = 2). The circles denote the regions illuminated with 405 nm laser (visible PBs or cytoplasmic residing DCP1). As a cautionary note, the cytoplasmic pool of DCP1 may not be in a dilute phase but could form diffraction-limited PBs. Note the high levels of EosFP-DCP1 signal (red) that shuttle from PBs to the PM, when PBs are photoconverted but not when the dilute phase is photoconverted. This result indicates that DCP1 can shuttle between PBs and the PM, although does not exclude that the cytoplasmic fraction of DCP1 can also shuttle. The offset of PBs post-conversion is due to their movement. Arrowheads denote DCP1 shuffling between the vertex/edge (see later for definition). Scale bars: 5 μm. Data information: In A, P values were determined with nested one-way ANOVA, and in B Wilcoxon. In boxplots or violin plots, upper and lower box boundaries, or lines in the violin plots when visible, represent the first and third quantiles, respectively, horizontal lines mark the median and whiskers mark the highest and lowest values.

Appendix Fig S2. DCP1/DCP2 or DCP1/PAT1 spatiotemporal interaction by PLA and SE-FRET.
A Sensitized emission (SE)-FRET efficiency between DCP2-YFP and mScarlet-DCP1 in lines co-expressing RPS5apro:HF-mScarlet-DCP1 and 35Spro:DCP2-YFP or PAT1pro:PAT1-GFP (cell vertices/edges; region 2, epidermal cells, 4 to 5-day-old, N=3, n=6-8). The cell contours are shown. Right: signal quantification of SE-FRET efficiency between the indicated combinations (DCP1/DCP2 and DCP1/PAT1). As a negative control, lines co-expressing GFP and mScarlet-DCP1 were used. Note also that FRET was observed for puncta in the cytoplasm. Max. int., maximum FRET intensity observed (spectrum scale on the right). Scale bars: 5 μm. B Left: the principle of the PLA method (see also main text). Duolink ® Proximity Ligation Assay (PLA) allows in situ detection of endogenous protein interactions with high specificity and sensitivity (theoretical PPI distance <40 nM). Protein targets can be readily detected and localized with single-molecule resolution. Typically, two primary or antibodies against epitope tags raised in different species are used to detect two unique protein targets. A pair of oligonucleotide-labeled secondary antibodies (PLA probes) bind to the primary antibodies. Next, hybridizing connector oligos join the PLA probes only if they are near each other, and ligase forms a closed, circle DNA template that is required for rolling-circle amplification (RCA). The PLA probe then acts as a primer for a DNA polymerase, which generates concatemeric sequences during RCA. This allows up to a 1,000-fold signal amplification that is still tethered

Appendix Fig S3. DCP1 is not strongly accumulating in BFA bodies.
Representative confocal micrographs showing the lack of effect from brefeldin A (BFA; 50 μM for 1 h) treatment on the DCP1-GFP (DCP1pro:DCP1-GFP). Scale bars: 20 μm. Arrowhead (top) denotes the edge/vertex, and BFA bodies (stained by FM4-64) are at the bottom. The experiment was performed three times with similar results irrespective of the tissue/cell type. BFA treatment in Arabidopsis roots causes the formation of TGN/endosomal agglomerates called BFA-induced bodies/compartments (Geldner et al, 2003). In "Mock", the endosomes in the cell are stained with FM4-64, and they do not represent small BFA bodies.

Appendix Fig S4. DCP1 is likely not recruited to the cell edge by Rab-A5c or SOK3.
A Representative confocal micrographs showing the localization of mScarlet-DCP1 (RPS5apro:HF-mScarlet-DCP1) and SOK3-YFP (SOK3pro:SOK3-YFP) from two regions as indicated (SOK3; left, no abnormal phenotype was observed for the root even for lines expressing SOK3pro:SOK3-YFP). The experiment was performed three times with similar results. Scale bars, 10 μm. Right: PLA showing a lack of interaction between DCP1 and SOK3 at the PM (HF-mScarlet-DCP1 + SOK3-YFP; α-FLAG + α-GFP, N, biological replicates = 2; Arrowheads indicate edges/vertices were DCP1 and SOK3 are not colocalized (in region 1) and colocalized (in region 3)). Scale bars: 10 μm. B Representative confocal micrographs showing the localization of DCP1, as detected by immunostaining with α-DCP1 (raised against full-length DCP1 protein purified from Escherichia coli; details in Materials and methods) and SOK3-YFP (SOK3pro:SOK3-YFP transgene; left, no phenotype was observed for the root) from two regions corresponding to 1 and 3. Scale bars: 7 µm. The confocal micrographs in boxes, show the detection of DCP1 using an α-DCP1 antibody in root meristematic epidermal cells of WT seedlings, dcp1-3 seedlings (with no specific signal), sok1 sok3 CRISPR, or seedlings expressing 35Spro:GFP-DCP1 (positive control) (N, biological replicates = 2). Note that the loss of SOK3 function does not affect DCP1 localization. The inset shows a detail of the vertex localization of DCP1; the yellow arrowheads point to the gap between two vertices in adjacent cells. Scale bars: 5 μm. C DCP1 and Rab-5Ac do not colocalize in dividing cells. Representative confocal micrographs showing the localization of DCP1 (detected by anti-DCP1) and Rab5-Ac-YFP in cytokinetic or mesophase cells (N=2). The white arrowhead denotes the accumulation of Rab5-Ac-YFP at the leading edge. Note the lack of DCP1 localization at the leading edge (offset of the yellow arrowhead in "Merge"). In the micrographs below, the edge/vertex is denoted by an arrowhead. Scale bars, 5 μm. Middle: propidium iodide (PI, 10 min)-stained roots showing the radial swelling of a whole root expressing the dexamethasone (DEX)-inducible line Dex>>RAB-A5c DN (2 μM DEX, 24 h; blue arrowhead). Under the same conditions, WT roots did not show any swelling. The diagram shows the progressive (an)isotropic growth between WT and Rab-A5c inducible lines, respectively. Upper right: a quantification of swollen cells in lines expressing RAB-A5cDN (N = 2, n = 10) is also shown. Lower right: DCP1 detection with the α-DCP1 antibody shows no differential localization of the protein upon dexamethasone induction (2 μM for up to 48 h). Scale bars, 5 μm. The graph indicates the percentage of cells with edge/vertex localization of DCP1 (N = 3, n = 5). Data information: In C, P were determined by an unpaired t-test. Upper and lower lines in the violin plots when visible, represent the first and third quantiles, respectively, horizontal lines mark the median and whiskers mark the highest and lowest values.

Appendix Fig S8. DCP1 can affect ARPC5 localization and spatial restriction at the edge.
Representative confocal micrographs showing the effect of DCP1 phosphovariants (as indicated in the images) in the localization of ARPC5-tagRFP (RPS5apro:ARPC5-tagRFP) in epidermal cells of region 2 or region 3 (meristematic zone and transition zone, respectively, N = 3, n = 8 cells). Upper right: calculation of the vertices which has both the signal of DCP1-GFP (phosphovariants) and ARPC5-tagRFP. Lower right: plot profiles indicating the relative signal intensity of DCP1-GFP (phosphovariants) and ARPC5-tagRFP signal along the cell edge and vertices. The edge size is shown by the green bar. Scale bars: 10 μm. Data information: The P values here were determined by Friedman (ordinary one-way ANOVA produced even lower P values <0.001). Upper and lower lines in the violin plots when visible, represent the first and third quantiles, respectively, horizontal lines mark the median and whiskers mark the highest and lowest values.